Visual Data-Driven Digital Twin Modeling Framework for Improving the Resilience of Urban Drainage Infrastructure Systems-Reference-Cited by-同舟云学术

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Visual Data-Driven Digital Twin Modeling Framework for Improving the Resilience of Urban Drainage Infrastructure Systems

Published:2024-01-25 Issue: Volume:16 Page:396-403
ISSN:
Container-title:Computing in Civil Engineering 2023
language:
Short-container-title:

Author:

Kim Jaeyoon¹²³,Thomas Aswin Jacob¹²³,Ham Youngjib¹²³

Affiliation:

1. Ph.D. Candidate, Dept. of Construction Science, Texas A&M Univ., College Station, TX.

2. M.S. Student, Dept. of Computer Science, Texas A&M Univ., College Station, TX.

3. History Maker Homes Endowed Associate Professor, Dept. of Construction Science, Texas A&M Univ., College Station, TX.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485224.048

Reference13 articles.

1. Geographic Information System application in flood risks prevention, hazards reduction and planning.;Adeyemi G. A.;IOP Conference Series: Materials Science and Engineering,2021

2. Automated localization of urban drainage infrastructure from public-access street-level images;Boller D.;Urban Water Journal,2019

3. Detecting and mapping traffic signs from Google Street View images using deep learning and GIS

4. Google. 2021. Discover Street View and contribute your own imagery to Google Maps.

5. Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making

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